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Volumn 4509 LNAI, Issue , 2007, Pages 159-170

Performance measures in classification of human communications

Author keywords

Evaluation measures; Human communication; Machine learning; Text classification

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA STRUCTURES; LEARNING SYSTEMS; MATRIX ALGEBRA;

EID: 37249057951     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-72665-4_14     Document Type: Conference Paper
Times cited : (17)

References (20)
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  • 2
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    • Machine learning in automated text categorization
    • Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34(1) (2002) 1-47
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  • 6
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    • Interestingness measures for data mining: A survey
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    • Geng, L.1    Hamilton, H.2
  • 7
    • 37249043418 scopus 로고    scopus 로고
    • Lallich, S., Teytaud, O., Prudhomme, E.: Association rules interestingness: measure and validation. In F., G., J., H.H., eds.: Quality Measures in Data Mining. Springer (2006)
    • Lallich, S., Teytaud, O., Prudhomme, E.: Association rules interestingness: measure and validation. In F., G., J., H.H., eds.: Quality Measures in Data Mining. Springer (2006)
  • 8
    • 1242308945 scopus 로고    scopus 로고
    • Selecting the right objective measure for association analysis
    • Tan, P., Kumar, V., Srivastava, J.: Selecting the right objective measure for association analysis. Information Systems 29(4) (2004) 293-313
    • (2004) Information Systems , vol.29 , Issue.4 , pp. 293-313
    • Tan, P.1    Kumar, V.2    Srivastava, J.3
  • 16
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    • Recognizing strong and weak opinion clauses
    • Wilson, T., Wiebe, J., Hwa, R.: Recognizing strong and weak opinion clauses. Computational Intelligence 22(2) (2006) 7399
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  • 17
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    • Text categorization with many redundant features: Using aggressive feature selection to make svms competitive with c4.5
    • Gabrilovich, E., Markovitch, S.: Text categorization with many redundant features: Using aggressive feature selection to make svms competitive with c4.5. In: Proc 21st International Conf on Machine Learning ICML'04. (2004) 321-328
    • (2004) Proc 21st International Conf on Machine Learning ICML'04 , pp. 321-328
    • Gabrilovich, E.1    Markovitch, S.2
  • 18
    • 84872201395 scopus 로고    scopus 로고
    • Sentiment classification on customer feedback data: Noisy data, large feature vectors, and the role of linguistic analysis
    • Gamon, M.: Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis. In: Proceedings of the 20th International Conference on Computational Linguistics (COLING 2004). (2004) 841-847
    • (2004) Proceedings of the 20th International Conference on Computational Linguistics (COLING , pp. 841-847
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    • Comparing diagnostic tests: A simple graphic using likelihood ratios
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.